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在紧急情况下为自动驾驶汽车提供基于风险的决策和控制策略

Hung Duy Nguyen¹, Mooryong Choi², Kyoungseok Han³

¹School of Mechanical Engineering, Kyungpook National University, Daegu 41566, South Korea; Automation & Control Institute (ACIN), TU Wien, Vienna 1040, Austria.

Accident; analysis and prevention

|September 23, 2023

概括

本研究介绍了在滑坡道路上自动驾驶汽车 (AV) 的风险知情决策. 这些方法通过防止碰撞和保持稳定性来确保正常和紧急驾驶期间的安全.

科学领域:

机器人技术机器人技术机器人技术
控制系统控制系统
人工智能的人工智能

背景情况:

自动驾驶汽车 (AV) 在恶劣的驾驶条件下面临挑战,特别是在滑动的道路上.
现有的决策算法可能对复杂的场景不够高效或强大.
确保车辆的安全性和稳定性对于AV采用至关重要.

研究的目的:

建议在严重的驾驶条件下为自动驾驶汽车提供基于风险的决策和控制方法.
为了确保AV在故障时可以安全地导航到指定区域.
通过防止后方碰撞和保持横向稳定性来提高AV安全性.

主要方法:

在正常的驾驶决策中使用了确定性有限状态机器 (FSM).
在紧急情况下使用碰撞时间 (TTC) 和减速率以避免碰撞 (DRAC) 实施风险评估.
通过轨迹优化 (Pontryagin最大原则) 生成安全路径,并使用线性时间变量模型预测控制 (LTV-MPC) 遵循它们.

主要成果:

与传统算法相比,拟议的FSM证明了效率.
风险指标 (TTC,DRAC) 在紧急情况下有效确定安全区.
在遵循优化轨迹的同时,LTV-MPC确保了AV横向稳定性.
模拟证实了对周围车辆和道路状况作出反应的适当的AV行为.

结论:

开发的基于风险的决策和控制策略对于在严重的驾驶条件下对AV有效.
综合方法提高了AV安全性和稳定性,特别是在滑动的道路上.
这些方法为在具有挑战性的环境和交通场景下自动驾驶提供了坚实的框架.

关键词:

自动驾驶汽车是一种自动驾驶汽车.避免碰撞,避免碰撞.决策方式决策方式有限状态机器的有限状态机器.模型预测控制模型预测控制最佳路径规划的最佳路径规划

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